System and method for making free-to-play and activity suggestions

ABSTRACT

The subject disclosure relates to methods for making user activity recommendations. In some aspects, a process of the technology can include operations for receiving a free-to-play indication, the free-to-play indication specifying availability of a user associated with a media system, retrieving, via the network interface, peer information indicating an availability of one or more online peers of the user, and retrieving, via the network interface, activity information indicating one or more activities available to the user, and at least one of the online peers. In some aspects method can further include operations for providing an activity recommendation to the user based on the peer information and the activity information, wherein the activity recommendation includes a suggestion of at least one activity that can be conducted by the user with the media system. Systems and computer-readable media are also provided.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/453,773 filed Mar. 8, 2017, which is incorporated by reference hereinin its entirety.

BACKGROUND 1. Field of the Invention

Aspects of the subject technology relate to providing user/playeractivity recommendations that the user can use to fill indicated periodsof free time. Some aspects of the technology also providerecommendations regarding friends or peers that are available toparticipate or collaborate in the recommended activity.

2. Description of the Related Art

Rapid growth of the Internet and the consequential proliferation ofonline gaming systems have resulted in significant changes in the numberand type of collaborative activities with which online users engage. Inaddition to online games, vendors provide, music, movies, socialnetworking streams and other media for consumption via specializedapplications (e.g., “apps”) executed on a personal computing platform,such as a console system, personal computer, smartphone, and/or tabletdevice, etc. To increase media consumption, content distributors, suchas Netflix have an incentive to provide targeted recommendations foradditional content items that may be of interest to the user.

SUMMARY OF THE CLAIMED INVENTION

Embodiments of the invention include systems and methods for makingactivity recommendations. Such systems may include one or moreprocessors, an input device coupled to the one or more processors, anetwork interface coupled to the one or more processors, andnon-transitory memory storing instructions for receiving, via the inputdevice, a free-to-play indication, the free-to-play indicationspecifying availability of a user associated with the media system,retrieving, via the network interface, peer information indicating anavailability of one or more online peers of the user; retrieving, viathe network interface, activity information, the activity informationindicating one or more activities available to the user and at least oneof the online peers; and providing an activity recommendation to theuser based on the peer information and the activity information, whereinthe activity recommendation comprises a suggestion of at least oneactivity that can be conducted by the user with the media system.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain features of the subject technology are set forth in the appendedclaims. However, the accompanying drawings, which are included toprovide further understanding, illustrate disclosed aspects and togetherwith the description serve to explain the principles of the subjecttechnology. In the drawings:

FIG. 1 illustrates an example environment in which some aspects of thetechnology can be implemented.

FIG. 2 illustrates steps of an example process for providing useractivity recommendations, according to some aspects of the technology.

FIG. 3 illustrates an example of an electronic system with which someaspects of the subject technology can be implemented.

FIG. 4 illustrates an example of a network device that can be used toimplement some aspects of the technology.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the disclosed technology and is not intendedto represent the only configurations in which the technology can bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a more thoroughunderstanding of the technology. However, it will be clear and apparentthat the technology is not limited to the specific details set forthherein and may be practiced without these details. In some instances,structures and components are shown in block diagram form in order toavoid obscuring the concepts of the subject technology.

Although content distributors typically provide recommendationsregarding the content they provide, such recommendations do not takeconsideration of general user activity preferences or preferences forconsumption of media from other sources. Additionally, media consumptionrecommendations (e.g., Netflix movie recommendations) do not account forthe collaborative nature of user engagement with certain content types,such as, watching a movie or playing an online game with a friend.

For computing platforms that tie together different content types fromdifferent providers (e.g., games, movies, music, and/or social mediafeeds, etc.), general user preferences can be given precedence over theincentive to promote a particular service. Accordingly, there exists aneed to provide accurate user activity recommendations in conjunctionwith recommendations regarding peer availability for potentialcollaboration.

Aspects of the disclosed technology address the foregoing need byproviding ways to make targeted user activity recommendations. Activityrecommendations can be provided in conjunction with a friend/usersuggestion, for example, to suggest an available peer that may beinterested in participating in the recommended activity (e.g., playing agame, or watching a television show). By providing accurate andgeneralized activity recommendations, user engagement with the computingplatform (e.g., game console system) can be encouraged, while alsoimproving the user experience though enhanced peer-to-peercollaboration.

Aspects of the technology involve determinations of activityrecommendations and/or peer recommendations made in response to a user's“free-to-play” indication. As used herein, a free-to-play indication caninclude any signal used to make inferences regarding free periods oftime in which the user may be interested in participation in a suggestedactivity. Free-to-play indications can include explicit user inputs toindicate a start time, a stop time, and/or a time duration in whichhe/she is free to participate in a suggested activity. In other aspects,free-to-play indications can be signals inferred from historic userbehaviors, and/or behaviors of other similarly situated users. Forexample, free-to-play indications may be outputs resulting from amachine-learning algorithm, such as a neural network, configured to makeinferences regarding a likelihood of player availability.

In response to a free-to-play indication, systems of the instanttechnology can provide recommendations for activities or media content auser may be interested in consuming via their associated device, suchas, a game console system. It is understood that the processing requiredto make activity and online peer recommendations can be performed by theuser's computing device, or performed using one or more remote systems,such as servers or computing clusters. As explained in further detailbelow, systems of the technology can also be configured to pullinformation from one or more third party networks, systems, or services,without departing from the scope of the technology. For example, throughaccount binding (e.g., with a user's Netflix, Pandora, and/or Facebookaccounts, etc.), computing systems used to implement the invention canretrieve data necessary to make activity and peer recommendations.

FIG. 1 illustrates an example environment 100 in which some aspects ofthe technology can be implemented. Environment 100 includes network 102,that permits communication between recommendation system 104,third-party provider 106, and users 108, 110, and 112, e.g., viarespective computing devices 108A, 110A, and 112A. Recommendation system104 contains recommendation module 104A that includes varioushardware/software modules for implementing aspects of the technology,including, application programming interface 104B, ActivityRecommendation Module 104C and Peer Recommendation Module 104D.

It is understood that the system architecture of environment 100 isintended to conceptually illustrate various functional components usedto provide activity and/or peer recommendations. However, a greater orfewer number of hardware and/or software components can be implemented.For example, recommendation system 104 could include multiple computingdevices (e.g., servers), as part of a network (e.g., an online gamingnetwork), or as part of a distributed computing system. Additionally,users/players 108, 110, and 112, are intended to help illustrate aspectsof the technology that relate to a multi-user platform or gamingenvironment; a greater number of players may be included, withoutdeparting from the scope of the technology.

Computing devices 108A, 110A, and 112A, can include any of a variety ofprocessor-based system types, including but not limited to one or moreof: gaming console/s, smartphone/s, tablet computing device/s, personalcomputer/s, and/or personal desktop assistant/s (PDAs), or the like.Additionally, as discussed in further detail below, activityrecommendation module 104C, and peer recommendation module 104D can beimplemented as separate software routines and/or hardware systems, e.g.,for providing different recommendations. Alternatively, activityrecommendation module 104C and peer recommendation module 104D can beimplemented as part of the same software system, e.g., instantiated onsimilar virtual machines, or as portions of the same machine-learningplatform.

In practice, recommendation system 104 receives a free-to-playindication corresponding with an indication of the player's (e.g.,user/player 108) period of availability. The free-to-play indication canbe received as a direct result of user input provided to computingdevice 108 (e.g., using a console controller or other input device), oras an inference drawn regarding a likelihood of user availability byrecommendation module 104A.

By way of example, user 108 can use a controller of game console 108A toindicate that he/she is available for the three hours beginning at 2 PM.Based on the received, free-to-play indication, recommendation system104 can begin aggregating information and performing processing neededto provide an activity recommendation to user 108, e.g., pertaining toactivities that can fill the indicated three hour window. For example,recommendation system 104 can pull data (i.e., activity information)from one or more media accounts of user 108, e.g., from third partyprovider 106, using API 104B. Information pulled by recommendationsystem 104 can include user history information, for example, pertainingto user purchases, content accessed (e.g., games played, musicdownloaded or movies watched, etc.), and/or social media information(e.g., friend lists, feeds, and/or social graphs, etc.), from thirdparty provider 106. Recommendation system 104 can also accessfree-to-play indications provided by other users, such as users 110,and/or 112.

Processing of activity information is then performed to identify one ormore suggested activities for user 108. Activity information processingcan be performed exclusively by recommendation system 104, for example,using activity recommendation module 104/peer recommendation module104D. Alternatively, processing can be performed at the associated userdevice, such as, console system 108, or a combination thereof.

Subsequently, an activity recommendation is provided to user 108indicating a suggestion of at least one activity that can be conductedby the user with media system 108A during the user's free time.Additionally, the activity recommendation can include an indication ofat least one other user, such as user 110 that can participate in therecommended activity.

Further to the above example, recommendation system 104 may determinethat user 108 is on level four of a specific game title that is alsoplayed by user 110. Based on similar progress in the game title, as wellas an overlapping availability as indicated by a free-to-play indicationprovided by user 110, activity recommendation system can provide user108 with an activity recommendation that he/she resume play of gametitle with user 110 at 2 PM. In this example the activity recommendationmay omit mention of user 112 for any of a variety of reasons. Forexample, user 112 may not be available during the indicated time period,or may not be a player of the game title. Alternatively, based onactivity history information and/or social history information of one ormore of uses 108, 110, and/or 112, it may be determined that user 108does not like to play the game title with user 112. Or that user 108 hasa strong affinity for playing the game title with user 110, etc.

FIG. 2 illustrates steps of an example process 200 for providing useractivity recommendations, according to some aspects of the technology.Process 200 begins with step 202 in which a free-to-play indication isreceived e.g., by a recommendation system (e.g., recommendation system104). As discussed above, the free-to-play indication can be issued by auser via an associated processor-based device (e.g., a game consolesystem or personal computer), or received by a recommendation system(e.g., recommendation system 104, discussed above). The free-to-playindication provides at least an indication of the user's availability,for example, to play an online game, watch a movie, or participate inanother activity facilitated by an associated user computing system.

In step 204, peer information is received indicating availability of oneor more online peers of the user. As with free-to-play information, peerinformation can be based on free-to-play indications provided by one ormore online peers, e.g., via an associated device or gaming system.Alternatively, peer information can be information inferred about theavailability of online peers, for example, using a machine-learningalgorithm configured to process data pertaining to the online peers,such as, relating to previous peer activities, or online peer-to-peerinteractions.

At step 206, activity information is retrieved indicating one or moreactivities available to the user and at least one online peer. Activityinformation can be received either directly from the users/online peers(e.g., via associated gaming consoles), and/or from one or more thirdparty providers, e.g., via an API configured to facilitate the transferof information from a bound third-party account to the recommendationsystem.

Activity information can include any data received/retrieved from one ormore content providers, such as Facebook, Netflix, Pandora, and/orTwitch, etc. As such, activity information can contain not onlyindications of activities (e.g., games) or media (movies, or songs,etc.) that the user may wish to consume, but also social data (e.g.,social history information) that can be used to determine which onlinepeers a user may be most interested in participating with.

Subsequently, at step 208, an activity recommendation is provided to theuser based on the peer information and the activity information. Theactivity recommendation can be based on processing performed on activityinformation associated with the user to determine a section of at leastone activity, from among multiple activities, that are available to theuser. Similarly, the activity recommendation can be based on processingperformed on social history information associated with the user todetermine a selection of online peers, with whom the user may beinterested in collaborative activities, i.e., online game play, or moviewatching, etc. As such, the activity recommendations may includesingular recommendations such as a single activity recommendation, or asingle activity/single peer recommendation. Alternatively, activityrecommendations may provide a list of a few activates and/or onlinepeers that may be of interest to the user.

After the user is provided with an activity of recommendation to fillhis/her free-to-play time slot, a user selection can be received. Forexample the user can select an activity, such as a particular TV showepisode to watch, or may select an activity in conjunction with aparticular online peer. By way of example, the user may elect to play aparticular video game title with a selected online peer, e.g., who hasachieved similar progress in the game, resulting in an automatic invitebeing sent to the online peer.

In some aspects, the user's activity/online peer selection can be suedto update social history information and/or activity history informationthat is associated with either the user, and/or one or more of theonline peers. Through the constant updating of activity historyinformation and social history information, adaptive machine-learningapproaches may be implemented to offer continuous improvements to theprovided activity recommendations.

FIG. 3 is an exemplary user device 300. User device 300 (e.g., desktop,laptop, tablet, mobile device, console gaming system) is a device thatthe user can utilize to facilitate carrying out features of the presentinvention pertaining communication with a recommendation system (e.g.,recommendation system 104), as discussed above.

User device 300 can include various elements as illustrated in FIG. 3.It should be noted that the elements are exemplary and that otherembodiments may incorporate more or less than the elements illustrated.With reference to FIG. 3, the user device 300 includes a main memory302, a central processing unit (CPU) 304, at least one vector unit 306,a graphics processing unit 308, an input/output (I/O) processor 310, anI/O processor memory 312, a controller interface 314, a memory card 316,a Universal Serial Bus (USB) interface 318, and an IEEE 1394 interface320, an auxiliary (AUX) interface 322 for connecting a tracking device324, although other bus standards and interfaces may be utilized. Theuser device 300 further includes an operating system read-only memory(OS ROM) 326, a sound processing unit 328, an optical disc control unit330, and a hard disc drive 332, which are connected via a bus 334 to theI/O processor 310. The user device 300 further includes at least onetracking device 324.

Tracking device 324 can be a camera, which includes eye-trackingcapabilities. The camera may be integrated into or attached as aperipheral device to user device 300. In some eye-tracking deviceimplementations, infrared non-collimated light is reflected from the eyeand sensed by a camera or optical sensor. The information is thenanalyzed to extract eye rotation from changes in reflections.Camera-based trackers focus on one or both eyes and record theirmovement as the viewer looks at some type of stimulus. Camera-based eyetrackers use the center of the pupil and light to create cornealreflections (CRs). The vector between the pupil center and the CR can beused to compute the point of regard on surface or the gaze direction. Asimple calibration procedure of the viewer is usually needed beforeusing the eye tracker.

Alternatively, more sensitive trackers use reflections from the front ofthe cornea and that back of the lens of the eye as features to trackover time. Even more sensitive trackers image features from inside theeye, including retinal blood vessels, and follow these features as theeye rotates. Most eye tracking devices use a sampling rate of at least30 Hz, although 50/60 Hz is most common. Some tracking devises run ashigh as 1250 Hz, which is needed to capture detail of very rapid eyemovement.

A range camera may instead be used with the present invention to capturegestures made by the user and is capable of facial recognition. A rangecamera is typically used to capture and interpret specific gestures,which allows a hands-free control of an entertainment system. Thistechnology may use an infrared projector, a camera, a depth sensor, anda microchip to track the movement of objects and individuals in threedimensions. This user device may also employ a variant of image-basedthree-dimensional reconstruction.

The tracking device 324 may include a microphone integrated into orattached as a peripheral device to user device 300 that captures voicedata. The microphone may conduct acoustic source localization and/orambient noise suppression.

Alternatively, tracking device 324 may be the controller of the userdevice 300. The controller may use a combination of built-inaccelerometers and infrared detection to sense its position in 3D spacewhen pointed at the LEDs in a sensor nearby, attached to, or integratedinto the console of the entertainment system. This design allows usersto control functionalities of the user device 300 with physical gesturesas well as button-presses. The controller connects to the user device300 using wireless technology that allows data exchange over shortdistances (e.g., 30 feet). The controller may additionally include a“rumble” feature (i.e., a shaking of the controller during certainpoints in the game) and/or an internal speaker.

The controller may additionally or alternatively be designed to capturebiometric readings using sensors in the remote to record data including,for example, skin moisture, heart rhythm, and muscle movement.

As noted above, the user device 300 may be an electronic gaming console.Alternatively, the user device 300 may be implemented as ageneral-purpose computer, a set-top box, or a hand-held gaming device.Further, similar user devices may contain more or less operatingcomponents.

CPU 304, vector unit 306, graphics processing unit 308, and I/Oprocessor 310 communicate via system bus 336. Further, the CPU 304communicates with the main memory 302 via a dedicated bus 338, while thevector unit 306 and the graphics processing unit 308 may communicatethrough a dedicated bus 340. The CPU 304 executes programs stored in theOS ROM 326 and the main memory 302. The main memory 302 may containpre-stored programs and programs transferred through the I/O Processor310 from a CD-ROM, DVD-ROM, or other optical disc (not shown) using theoptical disc control unit 332. The I/O processor 310 primarily controlsdata exchanges between the various devices of the user device 300including the CPU 304, the vector unit 306, the graphics processing unit308, and the controller interface 314.

Graphics processing unit 308 executes graphics instructions receivedfrom the CPU 304 and the vector unit 306 to produce images for displayon a display device (not shown). For example, the vector unit 306 maytransform objects from three-dimensional coordinates to two-dimensionalcoordinates, and send the two-dimensional coordinates to the graphicsprocessing unit 308. Furthermore, the sound processing unit 330 executesinstructions to produce sound signals that are outputted to an audiodevice such as speakers (not shown).

A user of the user device 300 provides instructions via the controllerinterface 314 to the CPU 304. For example, the user may instruct the CPU304 to store certain information on the memory card 316 or instruct theuser device 300 to perform some specified action.

Other devices may be connected to the user device 300 via the USBinterface 318, the IEEE 1394 interface 320, and the AUX interface 322.Specifically, a tracking device 324, including a camera or a sensor maybe connected to the user device 300 via the AUX interface 322, while acontroller may be connected via the USB interface 318.

FIG. 4 illustrates an example network device 410 according to someembodiments. Network device 410 can be used to implement one or moreservers or remote computing devices, such recommendation system 104,discussed above with respect to FIG. 1. Network device 410 includes amaster central processing unit (CPU) 462, interfaces 468, and a bus 415(e.g., a PCI bus). When acting under the control of appropriate softwareor firmware, the CPU 462 is responsible for executing packet management,error detection, and/or routing functions. The CPU 462 preferablyaccomplishes all these functions under the control of software includingan operating system and any appropriate applications software. CPU 462may include one or more processors 463 such as a processor from theMotorola family of microprocessors or the MIPS family ofmicroprocessors. In an alternative embodiment, processor 463 isspecially designed hardware for controlling the operations of router410. In a specific embodiment, memory 461 (such as non-volatile RAMand/or ROM) also forms part of CPU 462. However, there are manydifferent ways in which memory could be coupled to the system.

Interfaces 468 are typically provided as interface cards (sometimesreferred to as “line cards”). Generally, they control the sending andreceiving of data packets over the network and sometimes support otherperipherals used with the router 410. Among the interfaces that may beprovided are Ethernet interfaces, frame relay interfaces, cableinterfaces, DSL interfaces, token ring interfaces, and the like. Inaddition, various very high-speed interfaces may be provided such asfast token ring interfaces, wireless interfaces, Ethernet interfaces,Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POSinterfaces, FDDI interfaces and the like. Generally, these interfacesmay include ports appropriate for communication with the appropriatemedia. In some cases, they may also include an independent processorand, in some instances, volatile RAM. The independent processors maycontrol such communications intensive tasks as packet switching, mediacontrol and management. By providing separate processors for thecommunications intensive tasks, these interfaces allow the mastermicroprocessor 462 to efficiently perform routing computations, networkdiagnostics, security functions, etc.

Although the system shown in FIG. 4 is one specific network device ofthe present invention, it is by no means the only network devicearchitecture on which the present invention can be implemented. Forexample, an architecture having a single processor that handlescommunications as well as routing computations, etc. is often used.Further, other types of interfaces and media could also be used with therouter.

Regardless of the network device's configuration, it may employ one ormore memories or memory modules (including memory 461) configured tostore program instructions for the general-purpose network operationsand mechanisms for roaming, route optimization and routing functionsdescribed herein. The program instructions may control the operation ofan operating system and/or one or more applications, for example. Thememory or memories may also be configured to store tables such asmobility binding, registration, and association tables, etc.

It is understood that any specific order or hierarchy of steps in theprocesses disclosed is an illustration of exemplary approaches. Basedupon design preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged, or that only aportion of the illustrated steps be performed. Some of the steps may beperformed simultaneously. For example, in certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system components in the embodiments describedabove should not be understood as requiring such separation in allembodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but are to be accorded the full scope consistentwith the language claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.”

A phrase such as an “aspect” does not imply that such aspect isessential to the subject technology or that such aspect applies to allconfigurations of the subject technology. A disclosure relating to anaspect may apply to all configurations, or one or more configurations. Aphrase such as an aspect may refer to one or more aspects and viceversa. A phrase such as a “configuration” does not imply that suchconfiguration is essential to the subject technology or that suchconfiguration applies to all configurations of the subject technology. Adisclosure relating to a configuration may apply to all configurations,or one or more configurations. A phrase such as a configuration mayrefer to one or more configurations and vice versa.

The word “exemplary” is used herein to mean “serving as an example orillustration.” Any aspect or design described herein as “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs.

What is claimed is:
 1. A method for making in-media activityrecommendations, the method comprising: predicting a set of one or moreperiods of time during which a user is available to engage in anactivity via a media system, wherein predicting the set of availabletime periods is based on an evaluation of historic behavior dataregarding interaction with a plurality of content sources accessibleover a communication network, the historic behavior data from one ormore online accounts associated with the user; retrieving peerinformation over the communication network regarding one or more peersassociated with the user, wherein the retrieved peer informationindicates availability during the predicted available time periods andhistoric engagement with one or more content titles accessible from theplurality of content sources over the communication network; generatinga list of activity recommendations to present on a display deviceregarding the predicted set of available time periods of the user,wherein each activity recommendation specifies one of the content titlesand at least one of the peers having historic engagement with thespecified content title and having availability during one of theavailable time periods; tracking user selections from the list ofactivity recommendations presented on the display device, whereintracking the user selections comprises updating the historic behaviordata associated with the user; and generating a new list of activityrecommendations to present on the display device a new predicted set ofavailable time periods of the user based on the updated historicbehavior data.
 2. The method of claim 1, wherein at least one of thetracked user selections is associated with an identified one of thepeers, and further comprising updating the historic behavior dataassociated with the identified peer based on the tracked userselections.
 3. The method of claim 1, wherein the one or more onlineaccounts include social media accounts.
 4. The method of claim 1,further comprising identifying the one or more peers associated with theuser based on the online accounts associated with the user.
 5. Themethod of claim 1, wherein the tracked user selections include a peerselection from the one or more peers, wherein updating the historicbehavior data associated with the user is further based on the selectedpeer.
 6. The method of claim 1, wherein the one or more content titlesinclude at least one of a movie, a game, a song, a video, or a socialmedia feed.
 7. The method of claim 1, further comprising receivingupdated peer information, wherein generating the new list of activityrecommendations is further based on the updated peer information.
 8. Themethod of claim 1, further comprising identifying at least one peer assimilar to the user, wherein generating the list of activityrecommendations is further based on historic behavior data associatedwith the at least one peer identified as similar.
 9. The method of claim1, wherein generating the list of activity recommendations is furtherbased on a current level of progress of the user in relation to thespecified content title.
 10. A media system for making in-media activityrecommendations, the media system comprising: memory that storeshistoric behavior data from one or more online accounts associated witha user; and a processor coupled to a network interface and executesinstructions stored in memory, wherein execution of the instructions bythe processor: predicts a set of one or more periods of time duringwhich the user is available to engage in an activity via the mediasystem, wherein predicting the set of available time periods is based onan evaluation of the stored historic behavior data regarding interactionwith a plurality of content sources accessible over a communicationnetwork, retrieves peer information over the communication networkregarding one or more peers associated with the user, wherein theretrieved peer information indicates availability during the predictedavailable time periods and historic engagement with one or more contenttitles accessible from the plurality of content sources over thecommunication network, generates a list of activity recommendations topresent on a display device regarding the predicted set of availabletime periods of the user, wherein each activity recommendation specifiesone of the content titles and at least one of the peers having historicengagement with the specified content title and having availabilityduring one of the available time periods, tracks user selections fromthe list of activity recommendations presented on the display device,wherein tracking the user selections comprises updating the historicbehavior data associated with the user, and generates a new list ofactivity recommendations to present on the display device regarding anew predicted set of available time periods of the user based on theupdated historic behavior data.
 11. The media system of claim 10,wherein at least one of the tracked user selections is associated withan identified one of the peers, and wherein the processor executesfurther instruction to update the historic behavior data associated withthe identified peer based on the tracked user selections.
 12. The mediasystem of claim 10, wherein the one or more online accounts includesocial media accounts.
 13. The media system of claim 10, wherein theprocessor executes further instructions to identify the one or morepeers associated with the user based on the online accounts associatedwith the user.
 14. The media system of claim 10, further comprising auser interface that receives a peer selection from the one or morepeers, and wherein the memory further stores updated historic behaviordata associated with the user based on the selected peer.
 15. The mediasystem of claim 10, wherein the one or more content titles include atleast one of a movie, a game, a song, a video, or a social media feed.16. The media system of claim 10, further comprising a communicationinterface that receives updated peer information over a communicationnetwork, wherein the processor generates the new list of activityrecommendations based on the updated peer information.
 17. The system ofclaim 10, wherein the processor executes further instructions toidentify at least one peer as similar to the user, wherein the processorgenerates the list of activity recommendations further based on historicbehavior data associated with the at least one peer identified assimilar.
 18. The system of claim 10, wherein the processor generates thelist of activity recommendations further based on a current level ofprogress of the user in relation to the specified content title.
 19. Anon-transitory computer-readable storage medium, having embodied thereona program executable by a processor to perform a method for makingin-media activity recommendations, the method comprising: predicting aset of one or more periods of time during which a user is available toengage in an activity via a media system, wherein predicting the set ofavailable time periods is based on an evaluation of historic behaviordata regarding interaction with a plurality of content sourcesaccessible over a communication network, the historic behavior data fromone or more online accounts associated with the user; retrieving peerinformation over the communication network regarding one or more peersassociated with the user, wherein the retrieved peer informationindicates availability during the predicted available time periods andhistoric engagement with one or more content titles accessible from theplurality of content sources over the communication network; generatinga list of activity recommendations to present on a display deviceregarding the predicted set of available time periods of the user,wherein each activity recommendation specifies one of the content titlesand at least one of the peers having historic engagement with thespecified content title and having availability during one of theavailable time periods; tracking user selections from the list ofactivity recommendations presented on the display device, whereintracking the user selections comprises updating the historic behaviordata associated with the user; and generating a new list of activityrecommendations to present on the display device regarding a newpredicted set of available time periods of the user based on the updatedhistoric behavior data.